US 12,755,053 B2 · Scalable systems and methods for context-aware sensitive data detection, hierarchical labeling, and protection in natural language processing environments

Abstract

This disclosure relates to scalable systems and methods for detecting, labeling, and protecting sensitive data in natural language processing (NLP) environments. This includes NLP applications in artificial intelligence (AI) systems, such as language models (LMs) and generative AI (GenAI). More particularly, the present disclosure introduces a hierarchical, context-aware labeling mechanism that is optimized using an LM in conjunction with machine learning (ML) techniques to ensure the utility-preserving effective protection of sensitive data with, for example, minimal false positives and false negatives and/or optimal precision and recall. This disclosure also relates to data privacy, security, and context-sensitive access control, particularly for AI inference and data retrieval processes involving sensitive information in unstructured data. This disclosure also encompasses detection, labeling, tokenization, classification, provenance tracking, and selective disclosure enforcement, and extends to database-layer query authorization for sensitive unstructured data. The techniques disclosed herein may be applied to structured, semi-structured, and unstructured data formats.

Claims27

Dependent claims

A system, comprising:

a memory having, stored therein, computer program code;
and
one or more processing units operatively coupled to the memory and configured to execute instructions in the computer program code that cause the one or more processing units to:
analyze unstructured textual data using at least one natural language processing (NLP) technique;
detect sensitive information in the unstructured textual data according to at least one defined policy;
assign tokens to portions of the unstructured textual data indicating sensitivity classifications, wherein the sensitivity classifications are based, at least in part, on determined context-driven meanings of the respective portions of unstructured textual data;
and
associate token-based provenance metadata with at least some of the detected sensitive information, wherein the token-based provenance metadata is configured to provide for enforcement of context-sensitive access controls to the respective sensitive information post AI inference-time.

5 more elements

Dependencies are inferred from the claim text.

Scalable systems and methods for context-aware sensitive data detection, hierarchical labeling, and protection in natural language processing environments

Life of this patent

Scalable systems and methods for context-aware sensitive data detection, hierarchical labeling, and protection in natural language processing environments

Life of this patent

Abstract

Claims27